Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems
This paper proposes a computational technique for the classification of electricity consumption profiles. The methodology is comprised of two steps. In the first one, a C-means-based fuzzy clustering is performed in order to find consumers with similar consumption profiles. Afterwards, a fuzzy class...
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| Veröffentlicht in: | IEEE transactions on power delivery 2011-10, Vol.26 (4), p.2436-2442 |
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| container_title | IEEE transactions on power delivery |
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| creator | Angelos, Eduardo Werley S. Saavedra, O. R. Cortés, O. A. C. de Souza, A. N. |
| description | This paper proposes a computational technique for the classification of electricity consumption profiles. The methodology is comprised of two steps. In the first one, a C-means-based fuzzy clustering is performed in order to find consumers with similar consumption profiles. Afterwards, a fuzzy classification is performed using a fuzzy membership matrix and the Euclidean distance to the cluster centers. Then, the distance measures are normalized and ordered, yielding a unitary index score, where the potential fraudsters or users with irregular patterns of consumption have the highest scores. The approach was tested and validated on a real database, showing good performance in tasks of fraud and measurement defect detection. |
| doi_str_mv | 10.1109/TPWRD.2011.2161621 |
| format | Article |
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R.</creatorcontrib><creatorcontrib>Cortés, O. A. C.</creatorcontrib><creatorcontrib>de Souza, A. N.</creatorcontrib><title>Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems</title><title>IEEE transactions on power delivery</title><addtitle>TPWRD</addtitle><description>This paper proposes a computational technique for the classification of electricity consumption profiles. The methodology is comprised of two steps. In the first one, a C-means-based fuzzy clustering is performed in order to find consumers with similar consumption profiles. Afterwards, a fuzzy classification is performed using a fuzzy membership matrix and the Euclidean distance to the cluster centers. Then, the distance measures are normalized and ordered, yielding a unitary index score, where the potential fraudsters or users with irregular patterns of consumption have the highest scores. The approach was tested and validated on a real database, showing good performance in tasks of fraud and measurement defect detection.</description><subject>Abnormalities</subject><subject>Algorithm design and analysis</subject><subject>Applied sciences</subject><subject>Classification</subject><subject>Clustering methods</subject><subject>Data mining</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electrical power engineering</subject><subject>electricity theft</subject><subject>Energy consumption</subject><subject>Exact sciences and technology</subject><subject>Fraud</subject><subject>Fuzzy</subject><subject>fuzzy clustering</subject><subject>Fuzzy logic</subject><subject>Fuzzy reasoning</subject><subject>Fuzzy set theory</subject><subject>Miscellaneous</subject><subject>nontechnical losses</subject><subject>Power demand</subject><subject>Power networks and lines</subject><subject>Tasks</subject><subject>Testing. Reliability. 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N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-770b146814a5e95d99ea89037c79b78b34787e9b4c334e7efc9c2ac23c7e290c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Abnormalities</topic><topic>Algorithm design and analysis</topic><topic>Applied sciences</topic><topic>Classification</topic><topic>Clustering methods</topic><topic>Data mining</topic><topic>Electrical engineering. Electrical power engineering</topic><topic>Electrical power engineering</topic><topic>electricity theft</topic><topic>Energy consumption</topic><topic>Exact sciences and technology</topic><topic>Fraud</topic><topic>Fuzzy</topic><topic>fuzzy clustering</topic><topic>Fuzzy logic</topic><topic>Fuzzy reasoning</topic><topic>Fuzzy set theory</topic><topic>Miscellaneous</topic><topic>nontechnical losses</topic><topic>Power demand</topic><topic>Power networks and lines</topic><topic>Tasks</topic><topic>Testing. Reliability. Quality control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Angelos, Eduardo Werley S.</creatorcontrib><creatorcontrib>Saavedra, O. R.</creatorcontrib><creatorcontrib>Cortés, O. A. C.</creatorcontrib><creatorcontrib>de Souza, A. 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A. C.</au><au>de Souza, A. N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems</atitle><jtitle>IEEE transactions on power delivery</jtitle><stitle>TPWRD</stitle><date>2011-10-01</date><risdate>2011</risdate><volume>26</volume><issue>4</issue><spage>2436</spage><epage>2442</epage><pages>2436-2442</pages><issn>0885-8977</issn><eissn>1937-4208</eissn><coden>ITPDE5</coden><abstract>This paper proposes a computational technique for the classification of electricity consumption profiles. The methodology is comprised of two steps. In the first one, a C-means-based fuzzy clustering is performed in order to find consumers with similar consumption profiles. Afterwards, a fuzzy classification is performed using a fuzzy membership matrix and the Euclidean distance to the cluster centers. 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| subjects | Abnormalities Algorithm design and analysis Applied sciences Classification Clustering methods Data mining Electrical engineering. Electrical power engineering Electrical power engineering electricity theft Energy consumption Exact sciences and technology Fraud Fuzzy fuzzy clustering Fuzzy logic Fuzzy reasoning Fuzzy set theory Miscellaneous nontechnical losses Power demand Power networks and lines Tasks Testing. Reliability. Quality control |
| title | Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems |
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